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    <li class="toctree-l2"><a href="#_1">损失函数的使用</a></li>
    

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            <li><a class="toctree-l3" href="#mean_squared_error">mean_squared_error</a></li>
        
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                <h2 id="_1">损失函数的使用</h2>
<p>损失函数（或称目标函数、优化评分函数）是编译模型时所需的两个参数之一：</p>
<pre><code class="python">model.compile(loss='mean_squared_error', optimizer='sgd')
</code></pre>

<pre><code class="python">from keras import losses

model.compile(loss=losses.mean_squared_error, optimizer='sgd')
</code></pre>

<p>你可以传递一个现有的损失函数名，或者一个 TensorFlow/Theano 符号函数。
该符号函数为每个数据点返回一个标量，有以下两个参数:</p>
<ul>
<li><strong>y_true</strong>: 真实标签。TensorFlow/Theano 张量。</li>
<li><strong>y_pred</strong>: 预测值。TensorFlow/Theano 张量，其 shape 与 y_true 相同。</li>
</ul>
<p>实际的优化目标是所有数据点的输出数组的平均值。</p>
<p>有关这些函数的几个例子，请查看 <a href="https://github.com/keras-team/keras/blob/master/keras/losses.py">losses source</a>。</p>
<h2 id="_2">可用损失函数</h2>
<h3 id="mean_squared_error">mean_squared_error</h3>
<pre><code class="python">mean_squared_error(y_true, y_pred)
</code></pre>

<hr />
<h3 id="mean_absolute_error">mean_absolute_error</h3>
<pre><code class="python">mean_absolute_error(y_true, y_pred)
</code></pre>

<hr />
<h3 id="mean_absolute_percentage_error">mean_absolute_percentage_error</h3>
<pre><code class="python">mean_absolute_percentage_error(y_true, y_pred)
</code></pre>

<hr />
<h3 id="mean_squared_logarithmic_error">mean_squared_logarithmic_error</h3>
<pre><code class="python">mean_squared_logarithmic_error(y_true, y_pred)
</code></pre>

<hr />
<h3 id="squared_hinge">squared_hinge</h3>
<pre><code class="python">squared_hinge(y_true, y_pred)
</code></pre>

<hr />
<h3 id="hinge">hinge</h3>
<pre><code class="python">hinge(y_true, y_pred)
</code></pre>

<hr />
<h3 id="categorical_hinge">categorical_hinge</h3>
<pre><code class="python">categorical_hinge(y_true, y_pred)
</code></pre>

<hr />
<h3 id="logcosh">logcosh</h3>
<pre><code class="python">logcosh(y_true, y_pred)
</code></pre>

<p>预测误差的双曲余弦的对数。</p>
<p>对于小的 <code>x</code>，<code>log(cosh(x))</code> 近似等于 <code>(x ** 2) / 2</code>。对于大的 <code>x</code>，近似于 <code>abs(x) - log(2)</code>。这表示 'logcosh' 与均方误差大致相同，但是不会受到偶尔疯狂的错误预测的强烈影响。</p>
<p><strong>参数</strong></p>
<ul>
<li><strong>y_true</strong>: 目标真实值的张量。</li>
<li><strong>y_pred</strong>: 目标预测值的张量。</li>
</ul>
<p><strong>返回</strong></p>
<p>每个样本都有一个标量损失的张量。</p>
<hr />
<h3 id="categorical_crossentropy">categorical_crossentropy</h3>
<pre><code class="python">categorical_crossentropy(y_true, y_pred)
</code></pre>

<hr />
<h3 id="sparse_categorical_crossentropy">sparse_categorical_crossentropy</h3>
<pre><code class="python">sparse_categorical_crossentropy(y_true, y_pred)
</code></pre>

<hr />
<h3 id="binary_crossentropy">binary_crossentropy</h3>
<pre><code class="python">binary_crossentropy(y_true, y_pred)
</code></pre>

<hr />
<h3 id="kullback_leibler_divergence">kullback_leibler_divergence</h3>
<pre><code class="python">kullback_leibler_divergence(y_true, y_pred)
</code></pre>

<hr />
<h3 id="poisson">poisson</h3>
<pre><code class="python">poisson(y_true, y_pred)
</code></pre>

<hr />
<h3 id="cosine_proximity">cosine_proximity</h3>
<pre><code class="python">cosine_proximity(y_true, y_pred)
</code></pre>

<hr />
<p><strong>注意</strong>: 当使用 <code>categorical_crossentropy</code> 损失时，你的目标值应该是分类格式 (即，如果你有 10 个类，每个样本的目标值应该是一个 10 维的向量，这个向量除了表示类别的那个索引为 1，其他均为 0)。 为了将 <em>整数目标值</em> 转换为 <em>分类目标值</em>，你可以使用 Keras 实用函数 <code>to_categorical</code>：</p>
<pre><code class="python">from keras.utils.np_utils import to_categorical

categorical_labels = to_categorical(int_labels, num_classes=None)
</code></pre>
              
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